End-to-End Learned Driving on Public Roads

Modular autonomy stacks require extensive hand-engineering and city-by-city tuning that scales poorly.

An end-to-end approach learned driving behaviour directly from data, with a foundation-model backbone intended to generalise across environments.

The system drove in cities it had not been explicitly tuned for, suggesting a path to scaling autonomy without rebuilding rules for every new market.